pearl-H 0.1.1

PEARL: Prototype-guided Embedding Refinement via Adaptive Representation Learning

Prototype-Enhanced Aligned Representation Learning

Examples

Text Classification with BERT

from transformers import AutoTokenizer, AutoModel
from pearl import PEARLPipeline
import torch

# Extract BERT embeddings
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = AutoModel.from_pretrained('bert-base-uncased')

def get_embeddings(texts):
    inputs = tokenizer(texts, padding=True, truncation=True, return_tensors='pt')
    with torch.no_grad():
        outputs = model(**inputs)
    return outputs.last_hidden_state[:, 0, :].numpy()

# Get embeddings
X_train = get_embeddings(train_texts)
X_test = get_embeddings(test_texts)

# Apply PEARL
pearl = PEARLPipeline(n_classes=num_classes, device='cuda')
pearl.fit(X_train, y_train)

X_train_enhanced = pearl.transform(X_train, mode='paf')
X_test_enhanced = pearl.transform(X_test, mode='paf')

# Train classifier
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression()
clf.fit(X_train_enhanced, y_train)
accuracy = clf.score(X_test_enhanced, y_test)

See the examples/ directory for complete working examples:

• basic_usage.py: Simple synthetic data example
• text_classification.py: Real-world text classification with BERT

Performance

PEARL consistently improves classification performance across multiple benchmarks:

Dataset	Classifier	Raw F1	PEARL F1	Improvement
AG News	Logistic	0.8542	0.8876	+3.34%
AG News	SVM	0.8621	0.8912	+2.91%
AG News	MLP	0.8698	0.9045	+3.47%
AG News	RAG	0.8823	0.9156	+3.33%

Results show consistent improvements across different classifiers and datasets.

API Reference

PEARLPipeline

Main interface for PEARL.

Methods:

• fit(X_train, y_train, X_val, y_val, **kwargs): Train the pipeline
• transform(X, mode='paf'): Transform embeddings
• fit_transform(X, y, **kwargs): Fit and transform in one step
• save(path): Save pipeline to disk
• load(path, device): Load pipeline from disk (class method)

SignalExtractor

Neural network for signal extraction.

Methods:

• forward(x): Forward pass returning all outputs
• get_enhanced_embedding(x): Extract enhanced embedding

PrototypeFeatures

Prototype-based feature generator.

Methods:

• fit(embeddings, labels): Learn prototypes from training data
• transform(embeddings): Generate prototype features
• get_augmented(embeddings): Get embeddings + features

RAGClassifierWrapper

Retrieval-augmented classifier.

Methods:

• fit(X_train, y_train, X_val, y_val, **kwargs): Train the model
• predict(X): Predict class labels
• predict_proba(X): Predict class probabilities

Requirements

• Python >= 3.8
• PyTorch >= 1.12.0
• NumPy >= 1.20.0
• scikit-learn >= 1.0.0
• pandas >= 1.3.0
• openpyxl >= 3.0.0

Citation

If you use PEARL in your research, please cite:

@software{pearl2024,
  title={PEARL: Prototype-guided Embedding Refinement via Adaptive Representation Learning},
  author={PEARL Contributors},
  year={2024},
  url={https://github.com/yourusername/pearl}
}

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

Development Setup

git clone https://github.com/yourusername/pearl.git
cd pearl
pip install -e ".[dev]"

# Run tests
pytest tests/

# Format code
black pearl/

# Type checking
mypy pearl/

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

PEARL was developed to address the challenge of enhancing learned embeddings for downstream classification tasks. It builds on ideas from:

• Signal processing and denoising
• Prototype-based learning
• Multi-task learning
• Retrieval-augmented generation

Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: [e-mail]

Roadmap

• Support for additional embedding types (images, audio)
• Pre-trained models for common datasets
• Integration with popular frameworks (HuggingFace, PyTorch Lightning)
• Online/incremental learning support
• Multi-label classification support
• Comprehensive benchmarking suite

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Made with ❤️ by the PEARL team